class_docstr: Allow chatbots to be trained with the data from the Ubuntu Dialog Corpus.
docstr: Extract a tar file at the specified file path.
docstr_tok: ["Extract","a","tar","file","at","the","specified","file","path","."]
code: def extract(self, file_path):
      """
      Extract a tar file at the specified file path.
      """
      import tarfile
  
      print("Extracting {}".format(file_path))
  
      if not os.path.exists(self.extracted_data_directory):
          os.makedirs(self.extracted_data_directory)
  
      def track_progress(members):
          sys.stdout.write(".")
          for member in members:
              # This will be the current file being extracted
              yield member
  
      with tarfile.open(file_path) as tar:
          tar.extractall(
              path=self.extracted_data_directory,
              members=track_progress(tar),
          )
  
      self.chatbot.logger.info(
          "File extracted to {}".format(self.extracted_data_directory)
      )
  
      return True
  
imports: import os
  import sys
  import csv
  import time
  from dateutil import parser
  from chatterbot.conversation import Statement
  from chatterbot.tagging import PosLemmaTagger
  from chatterbot import utils
label_desc: Use image node_id 5 for calling the UbuntuCorpusTrainer obj's underlying member method code with example usage: obj.extract(file_path) and returns: True